油气集输管道腐蚀速率预测研究

腐蚀速率的精确预测对于油气集输管道的安全运行具有重要意义。鉴于神经网络算法陷入局部最小值、收敛速度慢和引起振荡效应等问题,同时考虑自适应遗传算法在广泛的空间搜索和向最优解的方向尽快收敛于最优目标的特点,构建了优化的混合算法神经网络模型。利用该模型对多种因素影响下的油气集输管道的腐蚀速度进行了预测研究。实际应用表明：该模型大大提高了网络的学习效率和预测评判的精度,可以作为油气集输管道腐蚀速率预测的良好工具。     

改进遗传算法校核管道摩阻系数

针对传统的管道摩阻系数校正模型带来较大误差的缺点,构建了一种新的优化数学模型;并提出一种改进遗传算法对其求解,结果表明:该算法能够很好地修正管道摩阻系数值,从而提高管道配水系统模型的效率和精度。     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

基于GPS-RTK的管道外检测数字化应用

为实现管道外检测信息数字化,结合GPS-RTK测量技术,将外检测数据与地理位置信息高度关联。利用该技术可实现外检测数据(如漏损点位置等)在空间上cm级精确定位,通过地图系统可直观查看检测数据在管道上的位置分布,为管道漏损点维修计划制订、现场开挖维修、内外检测数据对齐分析、管道中线测量等提供了精确的数据。     

基于神经网络的纳米SiO2改性陶粒混凝土性能预测

为实现对纳米SiO₂改性陶粒混凝土抗压强度和干缩值两项技术指标的预测,基于大量试验数据,以水胶比、纳米SiO₂掺量、陶粒用量为模型输入参数,提出了一种基于遗传算法优化改进的神经网络模型。结果表明,相比较于传统的回归分析法,经遗传算法优化的神经网络预测模型的拟合优度值(R²)超过了0.9,也可以更准确和稳定地预测纳米SiO₂改性陶粒混凝土的各项性能,该方法为解决绿色混凝土材料的配合比优化与性能调控问题提供了新思路。     

基于遗传算法的BP神经网络短时交通流预测

对BP神经网络模型、方法及遗传算法的基本原理进行了分析,将遗传算法与标准BP神经网络算法相结合构建了基于遗传算法的神经网络算法;建立了基于BP神经网络的短时交通流预测模型,对BP神经网络算法及基于遗传算法的BP神经网络算法进行设计,并将其应用于短时交通流预测模型的仿真计算,仿真结果表明基于遗传算法的BP神经网络算法具有更快的计算速度及更好的适应能力,该方法可以较好地应用于短时交通流预测。     

灰色GM(1,1)模型在靖西天然气管道负荷预测中的应用

靖西天然气管道是重要输气干线,准确预测需求负荷变化情况,确保管道安全、平稳、高效供气意义重大。文中以灰色理论为基础,利用管道历年气量数据建立灰色预测的GM(1,1)模型,采用后验差检验对预测模型进行检验,并对该管道未来用气需求量进行预测。计算结果显示:灰色GM(1,1)模型预测结果与实际结果具有较好的一致性,精度能够满足实际应用的要求,预测结果对靖西管道运行管理具有一定的借鉴作用。     

核级管道腐蚀数据的概率统计分析

针对核级管道腐蚀环境的复杂性及腐蚀过程的随机性,提出了基于概率统计方法的最大腐蚀深度预测模型。首先对核级管道进行腐蚀失效分析;其次采用广义极值分布模型（GEV）拟合管道最大腐蚀深度数据,用L-矩法计算模型的参数值,分析核级管道腐蚀深度的统计规律;最后引用回归期的概念预测管道最大腐蚀深度。以某核级管道为例,预测其最大腐蚀深度为4.575 1 mm,超过最大腐蚀深度的概率为0.75%。计算结果证明：应用极值理论作统计分析时,广义极值分布模型具有更广的适用性,该研究对分析腐蚀管道的可靠性和安全性具有一定的意义。     

腐蚀管道剩余寿命预测方法

基于可靠性理论,提出了一种预测管道腐蚀剩余寿命的新方法,即管道腐蚀可靠性寿命预测方法。该方法包括建立腐蚀管道的失效状态函数、腐蚀速率等变量的概率分布模型、管道失效概率和可靠度随时间的变化规律;然后根据管道所处地区级别和风险等级给定目标可靠度,确定管道的腐蚀剩余寿命。运用此方法预测了新疆采油一厂红浅注汽管道的腐蚀剩余寿命,为该管道腐蚀检测周期的确定提供了科学依据。     

川气东送天然气管道线路水合物形成预测

为了更好地预测、分析和处理水合物造成的不良后果,以川气东送管道为例讨论了天然气水合物的形成原因,提出了输气管线水合物预测模型,并建立了水合物形成预测的压力和温度计算模型,编制了“水合物预测软件”,预测该管道线路和场站的水合物形成条件和易形成水合物的区域.文中通过以上方法很好地预测了川气东送管道水合物的形成区域及情况,该预测方法可用于天然气管道线路水合物的形成预测.     

等里程间隔GM(1,1)模型在关角隧道6号斜井涌水量预测中的应用

在原始灰色GM(1,1)模型的基础上,通过运用等间距里程序列的分析方法建立模型,并对关角隧道6号斜井的涌水量进行了模拟预测。经检验,预测结果精度较高,对隧道工程涌水量的短期预测具有较大的实用价值。     

Dynamic travel time prediction using data clustering and genetic programming

The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modeling and predicting the expected, lower, and upper bounds of dynamic travel times along freeways. The models obtained from the genetic programming approach are algebraic expressions that provide insights into the spatiotemporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. Our algorithm is tested on a 37-mile freeway section encompassing several bottlenecks. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm and the historical average averaged over seven weekdays (p-value <0.0001). Specifically, the proposed algorithm achieves more than a 25% and 76% reduction in the prediction error over the instantaneous and historical average, respectively on congested days. When bagging is used in addition to the genetic programming, the results show that the mean width of the travel time interval is less than 5 min for the 60–80 min trip.     

TRIP DISTRIBUTION MODELLING USING FUZZY LOGIC AND A GENETIC ALGORITHM

This article examines possibilities for the application of soft computing techniques for the prediction of travel demand. The model, based on fuzzy logic and a genetic algorithm, successfully solves the trip distribution problem. The possibilities of using the proposed model in solving trip generation, modal split and route choice problems have also been indicated. The model has been tested on a real numerical example. Exceptionally good correspondences between estimated and real values of passenger flows have been obtained.     

Sustainable cold supply chain management under demand uncertainty and carbon tax regulation

Increasing awareness of sustainability in supply chain management has prompted organizations and individuals to consider environmental impacts when managing supply chains. The issues concerning environmental impacts are significant in cold supply chains due to substantial carbon emissions from storage and distribution of temperature-sensitive product. This paper investigates the impact of carbon emissions arising from storage and transportation in the cold supply chain in the presence of carbon tax regulation, and under uncertain demand. A two-stage stochastic programming model is developed to determine optimal replenishment policies and transportation schedules to minimize both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The performance and robustness of the matheuristic algorithm are analyzed using test instances in various sizes. A real-world case study in Queensland, Australia is used to demonstrate the application of the model. The results highlight that higher emissions price does not always contribute to the efficiency of the cold supply chain system. Furthermore, the analyses indicate that using heterogeneous fleet including light duty and medium duty vehicles can lead to further cost saving and emissions reduction.     

Reliable logistics networks design with facility disruptions

This paper studies a strategic supply chain management problem to design reliable networks that perform as well as possible under normal conditions, while also performing relatively well when disruptions strike. We present a mixed-integer programming model whose objective is to minimize the nominal cost (the cost when no disruptions occur) while reducing the disruption risk using the p-robustness criterion (which bounds the cost in disruption scenarios). We propose a hybrid metaheuristic algorithm that is based on genetic algorithms, local improvement, and the shortest augmenting path method. Numerical tests show that the heuristic greatly outperforms CPLEX in terms of solution speed, while still delivering excellent solution quality. We demonstrate the tradeoff between the nominal cost and system reliability, showing that substantial improvements in reliability are often possible with minimal increases in cost. We also show that our model produces solutions that are less conservative than those generated by common robustness measures.     

宜万铁路马鹿箐隧道进口泄水洞揭通溶腔施工技术

在宜万铁路马鹿箐隧道施工中,出口平导PDK255 978处发生了溶腔突水、突泥的情况,造成了重大突发性工程地质事故。鉴于溶腔规模大、水压高、储水量和动态补给量大等地质条件,采取了地表钻孔封堵平导、在平导进口左侧20 m处增加泄水洞的优化施工方案;结合现场实际情况,尽早揭通溶洞,并采取了直墙平底断面型式、锚喷初期支护以及取消二次衬砌、取消围幕注浆和管棚、采用光面爆破、加强超前地质预报等措施,施工中取得了较好的效果。     

Transport Network Design Problem under Uncertainty: A Review and New Developments

This paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP.     

An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems

Regenerative braking is an energy recovery mechanism that converts the kinetic energy during braking into electricity, also known as regenerative energy. In general, most of the regenerative energy is transmitted backward along the pantograph and fed back into the overhead contact line. To reduce the trains’ energy consumption, this paper develops a scheduling approach to coordinate the arrivals and departures of all trains located in the same electricity supply interval so that the energy regenerated from braking trains can be more effectively utilized to accelerate trains. Firstly, we formulate an integer programming model with real-world speed profiles to minimize the trains’ energy consumption with dwell time control. Secondly, we design a genetic algorithm and an allocation algorithm to find a good solution. Finally, we present numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results show that the proposed scheduling approach can reduce energy consumption by 6.97% and save about 1,054,388 CNY (or 169,223 USD) each year in comparison with the current timetable. Compared to the cooperative scheduling (CS) approach, the proposed scheduling approach can improve the utilization of regenerative energy by 36.16% and reduce the total energy consumption by 4.28%.     

Real time transit demand prediction capturing station interactions and impact of special events

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.